EduTwin AI A Learner-Centric Digital Twin and Retrieval-Augmented Generation platform for Personalized Higher Education
| dc.contributor.author | Ramdane ,Abdeldjalil | |
| dc.contributor.author | Guerriche Ala ,Abdel Moumene | |
| dc.contributor.author | Kerraoui Sarra | |
| dc.date.accessioned | 2026-10-28T09:23:54Z | |
| dc.date.available | 2026-10-28T09:23:54Z | |
| dc.date.issued | 2026 | |
| dc.description.abstract | The rapid evolution of digital education has exposed the limitations of traditional, "onesize- fits-all" learning management systems, highlighting an urgent need for intelligent, adaptive environments. This thesis presents EduTwin AI, an innovative platform designed to bridge the gap between static academic resources and the dynamic cognitive state of the learner. By natively integrating a Learner-Centric Digital Twin with a Retrieval-Augmented Generation (RAG) architecture, the system creates a continuous physical-digital feedback loop. The Digital Twin employs Bayesian Knowledge Tracing (BKT) to autonomously model and predict the student's concept mastery in real time. Simultaneously, the RAG pipeline acts as a strict pedagogical filter, ensuring that all interactions generated by the Large Language Model (LLM) are exclusively anchored to validated course materials, thereby eliminating AI hallucinations. Through adaptive assessments, personalized study paths, and a conversational tutor, EduTwin AI dynamically calibrates its complexity to match the student's Zone of Proximal Development. This work demonstrates the profound potential of combining real-time cognitive tracking with generative AI to deliver a truly personalized, secure, and academically rigorous learning experience. | |
| dc.identifier.uri | http://dspace.univ-skikda.dz:4000/handle/123456789/6531 | |
| dc.language.iso | en | |
| dc.publisher | Faculty of Sciences | |
| dc.title | EduTwin AI A Learner-Centric Digital Twin and Retrieval-Augmented Generation platform for Personalized Higher Education | |
| dc.title.alternative | Advanced Software Engineering and Applications | |
| dc.type | Thesis |